The bibliometric analysis of extended reality in surgical training: Global and Chinese perspective
Bibliographic record
Abstract
Objectives: The prospect of extended reality (XR) being integrated with surgical training curriculum has attracted scholars. However, there is a lack of bibliometric analysis to help them better understand this field. Our aim is to analyze relevant literature focusing on development trajectory and research directions since the 21st century to provide valuable insights. Methods: Papers were retrieved from the Web of Science Core Collection. Microsoft Excel, VOSviewer, and CiteSpace were used for bibliometric analysis. Results: Of the 3337 papers published worldwide, China contributed 204, ranking fifth. The world's enthusiasm for this field has been growing since 2000, whereas China has been gradually entering since 2001. Although China had a late start, its growth has accelerated since around 2016 due to the reform of the medical postgraduate education system and the rapid development of Chinese information technology, despite no research explosive period has been yet noted. International institutions, notably the University of Toronto, worked closely with others, while Chinese institutions lacked of international and domestic cooperation. Sixteen stable cooperation clusters of international scholars were formed, while the collaboration between Chinese scholars was not yet stable. XR has been primarily applied in orthopedic surgery, cataract surgery, laparoscopic training and intraoperative use in neurosurgery worldwide. Conclusions: There is strong enthusiasm and cooperation in the international research on the XR-based surgical training. Chinese scholars are making steady progress and have great potential in this area. There has not been noted an explosive research phase yet in the Chinese pace. The research on several surgical specialties has been summarized at the very first time. AR will gradually to be more involved and take important role of the research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.103 | 0.170 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".